Abstract
In practice, input data may come incrementally during data integration, static algorithm can’t adapt for this situation. So, to make truth discovery algorithm more practical, we present an incremental strategy in multisource integration using boosting like ensemble classifier. Our algorithm is adaptive for different update situations by considering concept drift in learning process. Our based model can treat entities inconsistently for a source also. These make truth finding more effective without repetitive computation.
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Jia, L., Wang, H., Li, J., Gao, H. (2013). Incremental Truth Discovery for Information from Multiple Data Sources. In: Gao, Y., et al. Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7901. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39527-7_8
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DOI: https://doi.org/10.1007/978-3-642-39527-7_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39526-0
Online ISBN: 978-3-642-39527-7
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